Cátia M. Salgado

Fuzzy modeling based on Mixed Fuzzy Clustering for health care applications

This papers proposes two novel approaches for the identification of Takagi-Sugeno fuzzy models with time variant and invariant features. The proposed Mixed Fuzzy Clustering algorithm is proposed for determining the parameters of Takagi-Sugeno fuzzy models in two different ways: (1) the antecedent fuzzy sets are determined based on the partition matrix generated by the Mixed Fuzzy Clustering algorithm; (2) the input features are transformed using the same algorithm and the antecedent fuzzy sets are derived using Fuzzy C-Means clustering. The proposed approaches are tested on four different health care applications: readmissions in intensive care units, administration of vasopressors and mortality. The results show that the proposed clustering algorithm resulted in an increase of the performance of the fuzzy models in three out of four applications in comparison to the use of Fuzzy C-Means.

Ensemble fuzzy classifiers design using weighted aggregation criteria

The rationale behind ensemble machine learning systems is the creation of many classifiers and the combination of their output such that the combination improves the performance of each single classifier. There are two key issues in the creation of ensemble classifiers: one is how two select and group the data samples to train the individual models and the other is how to select or combine the multiple outputs.

Multistage modeling for the classification of numerical and categorical datasets

Logistic regression and Takagi-Sugeno fuzzy models are sequentially trained with categorical and numerical data in an ensemble-based multistage scheme. In the first stage, a logistic regression model is used to transform the binary feature space into a numerical feature that is used to train a second stage of models consisting of an ensemble of two Takagi-Sugeno fuzzy models. In the ensemble, one model is trained in the space of numerical features and first stage prediction values. The other model is trained only with samples that were classified with a low degree of confidence by the first stage model, in the space of numerical variables. The final output is given by the average of the ensemble predictions at second stage. This scheme was devised under the hypothesis that separating binary from numerical features in the modeling process would increase the performance of a single model using both types of features together. The proposed multistage approach is used to solve a clinical classification problem in a Portuguese hospital. The problem consists of predicting comanagement signalling based on patient clinical data, including diagnosis, procedures, comorbidities and numerical scores, collected before surgery. The multistage performed better in the comanagement dataset, and in 2 out of 5 benchmark datasets.

Takagi–Sugeno Fuzzy Modeling Using Mixed Fuzzy Clustering

This paper proposes the use of mixed fuzzy clustering (MFC) algorithm to derive Takagi–Sugeno (T–S) fuzzy models (FMs). Mixed fuzzy clustering handles both time invariant and multivariate time variant features, allowing the user to control the weight of each component in the clustering process. Two model designs based on MFC are investigated. In the first, the antecedent fuzzy sets of the T–S model are obtained from the clusters obtained by the MFC algorithm. In the second, FMs based on fuzzy c-means (FCM) are constructed over the input space of the partition matrix generated by MFC. The proposed fuzzy modeling approaches are used in health care classification problems, where time series of unequal lengths are very common. MFC-based T–S FMs outperform FCM-based T–S FMs in four out of five datasets and k-nearest neighbors classifiers in five out of five datasets. Dynamic time warping performs better than the Euclidean distance in one dataset and similarly in the remaining. Given the different nature of time variant and invariant data, the choice of a clustering algorithm that treats data differently should be considered for model construction.

Mixed Fuzzy Clustering for Misaligned Time Series

Data mining in medical databases often involves the comparison of time series, which represent the evolution of a physiological variable. Temporal misalignment of physiological variables can conceal the discovery of patterns and trends shared between different patients. To address this problem, this paper proposes the mixed fuzzy clustering (MFC) algorithm with the dynamic time-warping (DTW) distance. We developed the MFC algorithm by 1) incorporating the DTW distance into the standard fuzzy c-means to handle misaligned time series; 2) introducing a new dimension into the spatiotemporal clustering algorithm to handle P time-variant features; and 3) incorporating unsupervised learning of cluster-dependent attribute weights. The algorithm is designed to simultaneously cluster time-variant and time-invariant data. We demonstrate the advantages of the proposed algorithm in four synthetic datasets and in two real-world applications in intensive care units. The first application is the classification of patients who will need the administration of vasopressors, and the second is the classification of patients with a high risk of mortality. Time-variant features consist of physiological variables collected with different sampling rates at different points in time. Time-invariant features consist of patients’ demographics and score records. The performance is evaluated using cluster validity measures, showing that the proposed algorithm outperforms fuzzy c-means.

Noise Versus Outliers

In this chapter, the reader will learn about methods for identifying outliers in a dataset, and how different methods can be compared.

Mixed Fuzzy Clustering for Deriving Predictive Models in Intensive Care Units

This chapter presents two novel approaches for the identification of Takagi-Sugeno fuzzy models with time variant and time invariant features. The mixed fuzzy clustering (MFC) algorithm is used for determining the parameters of Takagi-Sugeno fuzzy models (FMs) in two different ways: (1) MFC FM, where the antecedent fuzzy sets are determined based on the partition matrix generated by the mixed fuzzy clustering algorithm; (2) FCM–UMFC FM, where the input features are transformed using MFC and the antecedent fuzzy sets are derived using fuzzy c-means (FCM). The fuzzy modeling approaches are tested on four health care applications for the classification of critically ill patients: administration of vasopressors in pancreatitis and pneumonia patients, mortality in septic shock and early readmissions. Both approaches increase the performance of Takagi-Sugeno based on FCM, in all datasets. In particular, the best performer, FCM–UMFC FM, achieves notable improvements in the four datasets.

Clinical decision support tool for Co-management signalling

INTRODUCTION Co-management between internists and surgeons of selected patients is becoming one of the pillars of modern clinical management in large hospitals. Defining the patients to be co-managed is essential. The aim of this study is to create a decision tool using real-world patient data collected in the preoperative period, to support the decision on which patients should have the co-management service offered. METHODS Data was collected from the electronic clinical health records of patients who had an International Classification of Diseases, 9th edition (ICD-9) code of colorectal surgery during the period between January 2012 and October 2014 in a 200 bed private teaching hospital in Lisbon. ICD-9 codes of colorectal surgery [48.5 and 48.6 (anterior rectal resection and abdominoperineal resection), 45.7 (partial colectomy), 45.8 (Total Colectomy), and 45.9 (Bowel Anastomosis)] were used. Only patients above 18 years old were considered. Patients with more than one procedure were excluded from the study. From these data the authors investigated the construction of predictive models using logistic regression and Takagi-Sugeno fuzzy modelling. RESULTS Data contains information obtained from the clinical records of a cohort of 344 adult patients. Data from 398 emergent and elective surgeries were collected, from which 54 were excluded because they were second procedures for the same patients. Four preoperative variables were identified as being the most predictive of co-management, in multivariable regression analysis. The final model performed well after being internally validated (0.81 AUC, 77% accuracy, 74% sensitivity, 78% specificity, 93% negative predictive value). The results indicate that the decision process can be more objective and potentially automated. CONCLUSIONS The authors developed a prediction model based on preoperative characteristics, in order to support the decision for the co-management of surgical patients in the postoperative ward setting. The model is a simple bedside decision tool that uses only four numerical variables.

Short-term prediction of low kidney function in ICU patients

Intensive care treatment presents unique challenges in the medical world. When treating patients, their wide variety leave care providers with few past examples to draw on. Instead of operating in a pure knowledge discovery capacity, decision support systems can be developed to help predict short-term and long-term patient outcome, based upon available data. One area in which generalized severity scoring systems have consistently performed poorly is among patients admitted intensive care units (ICU) who then develop acute kidney injury. Urine output is used to guide fluid resuscitation and is one of the criteria for the diagnosis of acute kidney injury. This paper provides an example application for predicting short-term critical kidney function in an intensive care unit. Feature construction is performed to extract important aspects of the clinical evolution of the patient. Feature selection is performed on several patient features. Classifiers based on support vector machines and Takagi-Sugeno fuzzy models are developed to predict short-term drops in patient urine output rate. Both types of models showed comparable results, with an AUC of 78%. This shows potential in using similar classifiers to build an ICU decision support system with the goal of predicting short-term complication in the patient and augment current guidelines by anticipating treatment.

Daily prediction of ICU readmissions using feature engineering and ensemble fuzzy modeling

This research is focused on the prediction of ICU readmissions using fuzzy modeling and feature selection approaches. There are a number of published scores for assessing the risk of readmissions, but their poor predictive performance renders them unsuitable for implementation in the clinical setting. In this work, we propose the use of feature engineering and advanced computational intelligence techniques to improve the performance of current models. In particular, we propose an approach that relies on transforming raw vital signs, laboratory results and demographic information into more informative pieces of data, selecting a subset of relevant and nonredundant variables and applying fuzzy ensemble modeling to the featureengineered data for deriving important nonlinear relations between variables. Different criteria for selecting the best predictor from the ensemble and novel evaluation measures are explored. In particular, the area under the sensitivity curve and area under the specificity curve are investigated. The ensemble approach combined with feature transformation and feature selection showed increased performance, being able to predict early readmissions with an AUC of 0.77 0.02. To the best of our knowledge, this is the first computational intelligence technique allowing the prediction of readmissions in a daily basis. The high balance between sensitivity and specificity shows its strength and suitability for the management of the patient discharge decision making process.